scholarly journals Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis

2015 ◽  
Vol 55 (9) ◽  
pp. 1781-1803 ◽  
Author(s):  
Eleanor J. Gardiner ◽  
Valerie J. Gillet
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
Rough Set Theory ◽  
Formal Concept ◽  
Rule Mining ◽  
Emerging Patterns ◽  
Discovery Algorithms

Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules

2021 ◽  
pp. 1-25
Author(s):  
Tianxiong Wang ◽  
Meiyu Zhou
Keyword(s):  
Customer Satisfaction ◽  
Product Design ◽  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rough Set Theory ◽  
Mining Method ◽  
Rule Mining ◽  
Design Elements

When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics through quantitative analysis. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia

2020 ◽  
pp. 1436-1458
Author(s):  
Yuncheng Jiang ◽  
Mingxuan Yang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Formal Concept Analysis ◽  
Web Search ◽  
Rough Set Theory ◽  
Concept Analysis ◽  
Semantic Search ◽  
Formal Concept ◽  
Importance Ranking ◽  
Keyword Searches

This article describes how the traditional web search is essentially based on a combination of textual keyword searches with an importance ranking of the documents depending on the link structure of the web. However, one of the dimensions that has not been captured to its full extent is that of semantics. Currently, combining search and semantics gives birth to the idea of the semantic search. The purpose of this article is to present some new methods to semantic search to solve some shortcomings of existing approaches. Concretely, the authors propose two novel methods to semantic search by combining formal concept analysis, rough set theory, and similarity reasoning. In particular, the authors use Wikipedia to compute the similarity of concepts (i.e., keywords). The experimental results show that the authors' proposals perform better than some of the most representative similarity search methods and sustain the intuitions with respect to human judgements.

scholarly journals Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining

2012 ◽  
Vol 6-7 ◽  
pp. 625-630 ◽  
Author(s):  
Hong Sheng Xu
Keyword(s):  
Search Engine ◽  
Association Rule ◽  
Formal Concept Analysis ◽  
Association Rule Mining ◽  
High Efficiency ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Frequent Itemsets ◽  
Formal Concept ◽  
Rule Mining

In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

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